Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

TL;DR

This paper introduces the first federated collaborative filtering approach for privacy-preserving personalized recommendations, demonstrating that it maintains accuracy while enhancing user privacy in recommender systems.

Contribution

It presents a novel federated implementation of collaborative filtering using stochastic gradient updates, applicable to real-world datasets, with no loss in recommendation accuracy.

Findings

Federated collaborative filtering achieves comparable accuracy to traditional methods.

The approach enhances user privacy by keeping data on local devices.

Validated on MovieLens and proprietary datasets with consistent performance.

Abstract

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user' privacy, in contrast to the traditional paradigm of collecting, storing and processing user data on a backend server beyond the user's control. In this paper we introduce, as far as we are aware, the first federated implementation of a Collaborative Filter. The federated updates to the model are based on a stochastic gradient approach. As a classical case study in machine learning, we explore a personalized recommendation system based on users' implicit feedback and demonstrate the method's applicability to both the MovieLens and an in-house dataset. Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application while maintaining recommender performance.